1st Edition

Learning Analytics in Higher Education Current Innovations, Future Potential, and Practical Applications

    216 Pages 16 B/W Illustrations
    by Routledge

    216 Pages 16 B/W Illustrations
    by Routledge

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    Learning Analytics in Higher Education provides a foundational understanding of how learning analytics is defined, what barriers and opportunities exist, and how it can be used to improve practice, including strategic planning, course development, teaching pedagogy, and student assessment. Well-known contributors provide empirical, theoretical, and practical perspectives on the current use and future potential of learning analytics for student learning and data-driven decision-making, ways to effectively evaluate and research learning analytics, integration of learning analytics into practice, organizational barriers and opportunities for harnessing Big Data to create and support use of these tools, and ethical considerations related to privacy and consent. Designed to give readers a practical and theoretical foundation in learning analytics and how data can support student success in higher education, this book is a valuable resource for scholars and administrators.


    List of Tables

    List of Figures



    Chapter 1: Absorptive capacity and routines: Understanding barriers to learning analytics adoption in higher education
    Aditya Johri

    Chapter 2. Analytics in the field: Why locally grown continuous improvement systems are essential for effective data driven decision-making
    Matthew T. Hora

    Chapter 3: Big data, small data, and data shepherds
    Jennifer DeBoer and Lori Breslow

    Chapter 4: Evaluating scholarly teaching: A model and call for an evidence-based approach
    Daniel L. Reinholz, Joel C. Corbo, Daniel J. Bernstein, and Noah D. Finkelstein

    Chapter 5: Discipline-focused learning analytics approaches with users instead of for users
    David B. Knight, Cory Brozina, Timothy J. Kinoshita, Brian J. Novoselich, Glenda D. Young, and Jacob R. Grohs

    Chapter 6: Student consent in learning analytics: The devil in the details?
    Paul Prinsloo and Sharon Slade

    Chapter 7: Using learning analytics to improve student learning outcomes assessment in higher education: Potential, constraint, & possibility

    Carrie Klein, and Richard M. Hess

    Chapter 8: Data, data everywhere: Implications and considerations

    Matthew D. Pistilli

    Contributor Bios


    Jaime Lester is Associate Professor of Higher Education at George Mason University, USA.

    Carrie Klein is a PhD Candidate and Research and Teaching Assistant in the Higher Education Program at George Mason University, USA.

    Aditya Johri is Associate Professor of Information Sciences and Technology at George Mason University, USA.

    Huzefa Rangwala is Associate Professor of Computer Science at George Mason. University, USA.